In order to explore the influence of different losses on feature distribution more thoroughly,
we adopt t-SNE [1]} to visualize features of the samples from the FF++ dataset.
As is shown in Figure REF , some properties can be observed: a)
The learned features supervised by softmax loss appear as two clusters with neighboring boundaries.
b) The triplet loss has little effect on feature distribution. We have tried to increase the weight of triplet loss, but in such a case the network cannot converge normally.
c) The center loss significantly changes the distribution of features. However, constraining the intra-class compactness of manipulated faces leads to overfitting to some extent.
Hence, the performance gain is very small.
d) With the combination of SCL + softmax loss, the representations of natural faces are gathered compactly and separated from those of manipulated faces which are distributed less compactly.
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